diff --git a/README.md b/README.md
index cac18691..32b285d8 100644
--- a/README.md
+++ b/README.md
@@ -123,6 +123,9 @@ You will need the OpenAI API for all the agents.
 ```bash
 export OPENAI_API_KEY=$YOUR_OPENAI_API_KEY
 ```
+**Optional** Stock Price Data
+
+Since YFinance may not work, you can add alpha vantage api key in default_config.py.
 
 ### CLI Usage
 
diff --git a/tradingagents/agents/analysts/market_analyst.py b/tradingagents/agents/analysts/market_analyst.py
index 41ee944b..f7dab5ec 100644
--- a/tradingagents/agents/analysts/market_analyst.py
+++ b/tradingagents/agents/analysts/market_analyst.py
@@ -14,6 +14,8 @@ def create_market_analyst(llm, toolkit):
             tools = [
                 toolkit.get_YFin_data_online,
                 toolkit.get_stockstats_indicators_report_online,
+                toolkit.get_alpha_vantage_data,
+                toolkit.get_alpha_vantage_summary_signals,
             ]
         else:
             tools = [
diff --git a/tradingagents/agents/utils/agent_utils.py b/tradingagents/agents/utils/agent_utils.py
index 0b07f044..428c5fc0 100644
--- a/tradingagents/agents/utils/agent_utils.py
+++ b/tradingagents/agents/utils/agent_utils.py
@@ -161,6 +161,45 @@ class Toolkit:
 
         return result_data
 
+    @staticmethod
+    @tool
+    def get_alpha_vantage_data(
+            symbol: Annotated[str, "ticker symbol of the company"],
+            start_date: Annotated[str, "Start date in yyyy-mm-dd format"],
+            end_date: Annotated[str, "End date in yyyy-mm-dd format"],
+    ) -> str:
+        """
+        Retrieve the stock price data for a given ticker symbol from Alpha Vantage.
+        Args:
+            symbol (str): Ticker symbol of the company, e.g. AAPL, TSM
+            start_date (str): Start date in yyyy-mm-dd format
+            end_date (str): End date in yyyy-mm-dd format
+        Returns:
+            str: A formatted dataframe containing the stock price data for the specified ticker symbol in the specified date range.
+        """
+
+        result_data = interface.get_alpha_vantage_data(symbol, start_date, end_date)
+        return result_data
+
+    @staticmethod
+    @tool
+    def get_alpha_vantage_summary_signals(
+            symbol: Annotated[str, "ticker symbol of the company"],
+            start_date: Annotated[str, "Start date in yyyy-mm-dd format"],
+            end_date: Annotated[str, "End date in yyyy-mm-dd format"]
+    ) -> str:
+        """
+        Calculate technical indicators and signals based on stock price data from Alpha Vantage.
+        Args:
+            symbol (str): Ticker symbol of the company, e.g. AAPL, TSM
+            start_date (str): Start date in yyyy-mm-dd format
+            end_date (str): End date in yyyy-mm-dd format
+        Returns:
+            str: Formatted technical indicators summary and signals.
+        """
+        result_data = interface.get_alpha_vantage_summary_signals(symbol, start_date, end_date)
+        return result_data
+
     @staticmethod
     @tool
     def get_stockstats_indicators_report(
diff --git a/tradingagents/dataflows/interface.py b/tradingagents/dataflows/interface.py
index 7fffbb4f..d0c0a01f 100644
--- a/tradingagents/dataflows/interface.py
+++ b/tradingagents/dataflows/interface.py
@@ -805,3 +805,328 @@ def get_fundamentals_openai(ticker, curr_date):
     )
 
     return response.output[1].content[0].text
+
+def get_alpha_vantage_data(symbol, start_date, end_date):
+    config = get_config()
+    api_key = config["alpha_vantage_api_key"]
+
+    url = "https://www.alphavantage.co/query"
+    params = {
+        'function': 'TIME_SERIES_DAILY',
+        'symbol': symbol,
+        'outputsize': 'full',
+        'apikey': api_key
+    }
+
+    response = requests.get(url, params=params)
+    data = response.json()
+
+    print("API 响应:", list(data.keys()))  # 调试信息
+
+    if 'Time Series (Daily)' in data:
+        df = pd.DataFrame(data['Time Series (Daily)']).T
+        df.index = pd.to_datetime(df.index)
+        df = df.sort_index()
+
+        # 重命名列并转换数据类型
+        df.columns = ['Open', 'High', 'Low', 'Close', 'Volume']
+        for col in df.columns:
+            df[col] = pd.to_numeric(df[col])
+
+        # 过滤日期范围
+        if start_date:
+            df = df[df.index >= start_date]
+        if end_date:
+            df = df[df.index <= end_date]
+
+        # # Add header information
+        # header = f"# Stock data for {symbol.upper()} from {start_date} to {end_date}\n"
+        # header += f"# Total records: {len(data)}\n"
+        # header += f"# Data retrieved on: {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}\n\n"
+
+        return df
+    else:
+        print("错误或限制:", data)
+        return None
+
+
+def add_technical_indicators(df):
+    """
+    为股票数据添加技术指标
+
+    参数:
+        df: DataFrame with columns ['Open', 'High', 'Low', 'Close', 'Volume']
+
+    返回:
+        DataFrame with additional technical indicator columns
+    """
+
+    # 创建副本以避免修改原数据
+    data = df.copy()
+
+    # 确保数据按日期排序
+    data = data.sort_index()
+
+    # =============================================================================
+    # Moving Averages 移动平均线
+    # =============================================================================
+
+    # 50日简单移动平均线
+    data['close_50_sma'] = data['Close'].rolling(window=50).mean()
+
+    # 200日简单移动平均线
+    data['close_200_sma'] = data['Close'].rolling(window=200).mean()
+
+    # 10日指数移动平均线
+    data['close_10_ema'] = data['Close'].ewm(span=10).mean()
+
+    # =============================================================================
+    # MACD 指标
+    # =============================================================================
+
+    # 计算12日和26日EMA
+    ema_12 = data['Close'].ewm(span=12).mean()
+    ema_26 = data['Close'].ewm(span=26).mean()
+
+    # MACD线 = 12EMA - 26EMA
+    data['macd'] = ema_12 - ema_26
+
+    # MACD信号线 = MACD的9日EMA
+    data['macds'] = data['macd'].ewm(span=9).mean()
+
+    # MACD柱状图 = MACD - 信号线
+    data['macdh'] = data['macd'] - data['macds']
+
+    # =============================================================================
+    # RSI 相对强弱指数
+    # =============================================================================
+
+    def calculate_rsi(prices, period=14):
+        """计算RSI"""
+        delta = prices.diff()
+        gain = delta.where(delta > 0, 0)
+        loss = -delta.where(delta < 0, 0)
+
+        avg_gain = gain.rolling(window=period).mean()
+        avg_loss = loss.rolling(window=period).mean()
+
+        rs = avg_gain / avg_loss
+        rsi = 100 - (100 / (1 + rs))
+        return rsi
+
+    data['rsi'] = calculate_rsi(data['Close'])
+
+    # =============================================================================
+    # Bollinger Bands 布林带
+    # =============================================================================
+
+    # 布林带中线（20日SMA）
+    data['boll'] = data['Close'].rolling(window=20).mean()
+
+    # 计算20日标准差
+    std_20 = data['Close'].rolling(window=20).std()
+
+    # 布林带上轨 = 中线 + 2倍标准差
+    data['boll_ub'] = data['boll'] + (2 * std_20)
+
+    # 布林带下轨 = 中线 - 2倍标准差
+    data['boll_lb'] = data['boll'] - (2 * std_20)
+
+    # =============================================================================
+    # ATR 平均真实波幅
+    # =============================================================================
+
+    def calculate_atr(high, low, close, period=14):
+        """计算ATR"""
+        # 真实波幅的三种计算方式
+        tr1 = high - low
+        tr2 = abs(high - close.shift(1))
+        tr3 = abs(low - close.shift(1))
+
+        # 取最大值作为真实波幅
+        true_range = pd.concat([tr1, tr2, tr3], axis=1).max(axis=1)
+
+        # ATR = 真实波幅的移动平均
+        atr = true_range.rolling(window=period).mean()
+        return atr
+
+    data['atr'] = calculate_atr(data['High'], data['Low'], data['Close'])
+
+    # =============================================================================
+    # VWMA 成交量加权移动平均
+    # =============================================================================
+
+    def calculate_vwma(close, volume, period=20):
+        """计算成交量加权移动平均"""
+        volume_price = close * volume
+        vwma = volume_price.rolling(window=period).sum() / volume.rolling(window=period).sum()
+        return vwma
+
+    data['vwma'] = calculate_vwma(data['Close'], data['Volume'])
+
+    # =============================================================================
+    # 额外计算一些有用的信号
+    # =============================================================================
+
+    # 金叉死叉信号 (50 SMA vs 200 SMA)
+    data['golden_cross'] = (data['close_50_sma'] > data['close_200_sma']) & \
+                           (data['close_50_sma'].shift(1) <= data['close_200_sma'].shift(1))
+
+    data['death_cross'] = (data['close_50_sma'] < data['close_200_sma']) & \
+                          (data['close_50_sma'].shift(1) >= data['close_200_sma'].shift(1))
+
+    # MACD信号
+    data['macd_bullish'] = (data['macd'] > data['macds']) & \
+                           (data['macd'].shift(1) <= data['macds'].shift(1))
+
+    data['macd_bearish'] = (data['macd'] < data['macds']) & \
+                           (data['macd'].shift(1) >= data['macds'].shift(1))
+
+    # RSI信号
+    data['rsi_overbought'] = data['rsi'] > 70
+    data['rsi_oversold'] = data['rsi'] < 30
+
+    # 布林带信号
+    data['price_above_upper_band'] = data['Close'] > data['boll_ub']
+    data['price_below_lower_band'] = data['Close'] < data['boll_lb']
+
+    # 显示统计信息
+    total_indicators = 11  # 主要技术指标数量
+    valid_rows = len(data.dropna())
+
+    return data
+
+
+def display_indicator_summary(df):
+    """Generate technical indicator summary string"""
+
+    latest = df.iloc[-1]  # Latest data
+
+    summary = f"\n📈 Latest Technical Indicator Summary ({latest.name.date()}):\n"
+    summary += "=" * 50 + "\n"
+
+    # Price information
+    summary += f"Close Price: ${latest['Close']:.2f}\n"
+
+    # Moving averages
+    summary += f"\n📊 Moving Averages:\n"
+    if not pd.isna(latest['close_10_ema']):
+        summary += f"   10-day EMA: ${latest['close_10_ema']:.2f}\n"
+    if not pd.isna(latest['close_50_sma']):
+        summary += f"   50-day SMA: ${latest['close_50_sma']:.2f}\n"
+    if not pd.isna(latest['close_200_sma']):
+        summary += f"   200-day SMA: ${latest['close_200_sma']:.2f}\n"
+
+    # Trend signals
+    if not pd.isna(latest['close_50_sma']) and not pd.isna(latest['close_200_sma']):
+        trend = "Uptrend 📈" if latest['close_50_sma'] > latest['close_200_sma'] else "Downtrend 📉"
+        summary += f"   Long-term Trend: {trend}\n"
+
+    # MACD
+    summary += f"\n📈 MACD:\n"
+    if not pd.isna(latest['macd']):
+        summary += f"   MACD: {latest['macd']:.4f}\n"
+        summary += f"   Signal Line: {latest['macds']:.4f}\n"
+        summary += f"   Histogram: {latest['macdh']:.4f}\n"
+
+        if latest['macd'] > latest['macds']:
+            summary += "   📈 MACD Bullish Signal\n"
+        else:
+            summary += "   📉 MACD Bearish Signal\n"
+
+    # RSI
+    summary += f"\n⚡ RSI:\n"
+    if not pd.isna(latest['rsi']):
+        summary += f"   RSI(14): {latest['rsi']:.2f}\n"
+        if latest['rsi'] > 70:
+            summary += "   ⚠️  Overbought Zone\n"
+        elif latest['rsi'] < 30:
+            summary += "   ⚠️  Oversold Zone\n"
+        else:
+            summary += "   ✅ Normal Zone\n"
+
+    # Bollinger Bands
+    summary += f"\n📊 Bollinger Bands:\n"
+    if not pd.isna(latest['boll']):
+        summary += f"   Upper Band: ${latest['boll_ub']:.2f}\n"
+        summary += f"   Middle Band: ${latest['boll']:.2f}\n"
+        summary += f"   Lower Band: ${latest['boll_lb']:.2f}\n"
+
+        if latest['Close'] > latest['boll_ub']:
+            summary += "   📈 Price Above Upper Band\n"
+        elif latest['Close'] < latest['boll_lb']:
+            summary += "   📉 Price Below Lower Band\n"
+        else:
+            summary += "   ✅ Price Within Bands\n"
+
+    # ATR and VWMA
+    summary += f"\n📊 Other Indicators:\n"
+    if not pd.isna(latest['atr']):
+        summary += f"   ATR(14): {latest['atr']:.2f} (Volatility)\n"
+    if not pd.isna(latest['vwma']):
+        summary += f"   VWMA(20): ${latest['vwma']:.2f} (Volume-Weighted MA)\n"
+
+    return summary
+
+
+def get_trading_signals(df):
+    """Get recent trading signals as string"""
+
+    recent_data = df.tail(5)  # Last 5 days
+    signals = []
+
+    for date, row in recent_data.iterrows():
+        day_signals = []
+
+        if row.get('golden_cross', False):
+            day_signals.append("🟢 Golden Cross")
+        if row.get('death_cross', False):
+            day_signals.append("🔴 Death Cross")
+        if row.get('macd_bullish', False):
+            day_signals.append("📈 MACD Bullish")
+        if row.get('macd_bearish', False):
+            day_signals.append("📉 MACD Bearish")
+        if row.get('rsi_overbought', False):
+            day_signals.append("⚠️ RSI Overbought")
+        if row.get('rsi_oversold', False):
+            day_signals.append("⚠️ RSI Oversold")
+        if row.get('price_above_upper_band', False):
+            day_signals.append("📈 Above Upper Band")
+        if row.get('price_below_lower_band', False):
+            day_signals.append("📉 Below Lower Band")
+
+        if day_signals:
+            signals.append(f"{date.date()}: {', '.join(day_signals)}")
+
+    if signals:
+        result = "\n🚨 Recent Trading Signals:\n"
+        result += "=" * 30 + "\n"
+        for signal in signals:
+            result += f"   {signal}\n"
+        return result
+    else:
+        return "\n✅ No significant trading signals in the last 5 days\n"
+
+def get_alpha_vantage_summary_signals(symbol, start_date, end_date):
+    # 1. 获取股票数据 (使用前面的Alpha Vantage函数)
+    data = get_alpha_vantage_data(symbol, start_date, end_date)
+
+    # 2. 添加技术指标
+    data_with_indicators = add_technical_indicators(data)
+
+    # 3. 查看指标摘要
+    summary = display_indicator_summary(data_with_indicators)
+
+    # 4. 查看交易信号
+    signals = get_trading_signals(data_with_indicators)
+
+    # 5. 查看特定指标
+    df_tail = data_with_indicators[['Close', 'rsi', 'macd', 'close_50_sma']].tail()
+    tail_string = "\nRecent Prices\n" + df_tail.to_string()
+
+    return summary + signals + tail_string
+
+
+if __name__ == "__main__":
+    text = get_alpha_vantage_summary_signals('NVDA', '2024-01-01', '2024-12-31')
+    print(text)
diff --git a/tradingagents/default_config.py b/tradingagents/default_config.py
index 089e9c24..d70b1c10 100644
--- a/tradingagents/default_config.py
+++ b/tradingagents/default_config.py
@@ -19,4 +19,5 @@ DEFAULT_CONFIG = {
     "max_recur_limit": 100,
     # Tool settings
     "online_tools": True,
+    "alpha_vantage_api_key": "$YOUR_API_KEY"
 }
diff --git a/tradingagents/graph/trading_graph.py b/tradingagents/graph/trading_graph.py
index 80a29e53..b5d83953 100644
--- a/tradingagents/graph/trading_graph.py
+++ b/tradingagents/graph/trading_graph.py
@@ -117,6 +117,8 @@ class TradingAgentsGraph:
                     # online tools
                     self.toolkit.get_YFin_data_online,
                     self.toolkit.get_stockstats_indicators_report_online,
+                    self.toolkit.get_alpha_vantage_data,
+                    self.toolkit.get_alpha_vantage_summary_signals,
                     # offline tools
                     self.toolkit.get_YFin_data,
                     self.toolkit.get_stockstats_indicators_report,
@@ -127,7 +129,7 @@ class TradingAgentsGraph:
                     # online tools
                     self.toolkit.get_stock_news_openai,
                     # offline tools
-                    self.toolkit.get_reddit_stock_info,
+                    # self.toolkit.get_reddit_stock_info,
                 ]
             ),
             "news": ToolNode(
@@ -136,8 +138,8 @@ class TradingAgentsGraph:
                     self.toolkit.get_global_news_openai,
                     self.toolkit.get_google_news,
                     # offline tools
-                    self.toolkit.get_finnhub_news,
-                    self.toolkit.get_reddit_news,
+                    # self.toolkit.get_finnhub_news,
+                    # self.toolkit.get_reddit_news,
                 ]
             ),
             "fundamentals": ToolNode(
@@ -145,11 +147,11 @@ class TradingAgentsGraph:
                     # online tools
                     self.toolkit.get_fundamentals_openai,
                     # offline tools
-                    self.toolkit.get_finnhub_company_insider_sentiment,
-                    self.toolkit.get_finnhub_company_insider_transactions,
-                    self.toolkit.get_simfin_balance_sheet,
-                    self.toolkit.get_simfin_cashflow,
-                    self.toolkit.get_simfin_income_stmt,
+                    # self.toolkit.get_finnhub_company_insider_sentiment,
+                    # self.toolkit.get_finnhub_company_insider_transactions,
+                    # self.toolkit.get_simfin_balance_sheet,
+                    # self.toolkit.get_simfin_cashflow,
+                    # self.toolkit.get_simfin_income_stmt,
                 ]
             ),
         }